___________________________________________________________________________________________________________________ ATTRACTION pGNAT ___________________________________________________________________________________________________________________ Script Author: Marisa Okano last updated: 10-07-2024 by K. Borchert (katjab@millisecond.com) for Millisecond Software, LLC Millisecond Software thanks Marisa Okano for sharing her script! ___________________________________________________________________________________________________________________ BACKGROUND INFO ___________________________________________________________________________________________________________________ This “Attraction pGNAT” is a modified version of the Go/No-go Association Task (GNAT), which was originally designed to measure automatic associations regarding a single target construct (Nosek & Banaji, 2001). Specifically, the same basic procedure of the GNAT is used to assess the strength of participants' positive versus negative associations regarding a single target category (e.g. physical attractiveness); however, the GNAT will be modified to reflect the personalized version utilized in the study of explicit and implicit preferences of physical attractiveness by Eastwick, Eagly, Finkel, & Johnson (2011). In general, the GNAT uses uses the Go-Nogo framework of responding to signal and noise stimuli to investigate implicit bias. In contrast to reaction time based tests of implicit bias (e.g. Implicit Association Test), the GNAT framework mainly focuses on accuracy data and specifically d prime measures (measures of sensitivity to distinguish signals from noise in signal detection theory) to infer implicit bias. For example, a positive association of 'physical attractiveness' (the target signal) is suggested if the dprime measure in the condition 'Physical Attractiveness-I like' (both of these categories are signals) is greater than the dprime measure in the condition 'Physical Attractiveness-I don't like' (both of these categories are signals). This script investigates the following three categories: PA = physical appearance SS = social status PE = personality by pairing them with the attribute categories 'I like' vs. 'I don't like' The implemented procedure of the “Attraction pGNAT” is based on: Eastwick, P. W., Eagly, A. H., Finkel, E. J., & Johnson, S. E. (2011). Implicit and explicit preferences for physical attractiveness in a romantic partner: A double dissociation in predictive validity. Journal of Personality and Social Psychology, 101, 993-1011. Adjustments to z-scores as described by: Gregg, A. & Sedikides, C. (2010). Narcissistic Fragility: Rethinking Its Links to Explicit and Implicit Self-esteem, Self and Identity, 9:2, 142-161 (p.148) ___________________________________________________________________________________________________________________ TASK DESCRIPTION ___________________________________________________________________________________________________________________ Participants are asked to categorize items (e.g words "attractive", "wealthy" etc) into predetermined categories via keystroke presses. The basic task is to press the Spacebar if an items (e.g. "attractive") belongs to the category currently being tested (e.g. Physical Appearance) and to do nothing if it doesn't. For practice, participants sort items into categories "Physical Appearance", "Social Status", "Personality" as well as evaluative preference categories "I like" and "I don't like". For the test, participants are asked to sort categories into the paired/combined categories (e.g. "Physical Appearance OR I don't like"). When an item belongs to either one of these two categories, participants should press the Spacebar. ___________________________________________________________________________________________________________________ DURATION ___________________________________________________________________________________________________________________ the default set-up of the script takes appr. 20 minutes to complete ___________________________________________________________________________________________________________________ DATA FILE INFORMATION ___________________________________________________________________________________________________________________ The default data stored in the data files are: (1) Raw data file: 'attraction_pgnat_raw*.iqdat' (a separate file for each participant) build: The specific Inquisit version used (the 'build') that was run computer.platform: the platform the script was run on (win/mac/ios/android) date, time: date and time script was run subject, group: with the current subject/groupnumber session: with the current session id blockcode, blocknum: the name and number of the current block (built-in Inquisit variable) trialcode, trialnum: the name and number of the currently recorded trial (built-in Inquisit variable) Note: trialnum is a built-in Inquisit variable; it counts all trials run; even those that do not store data to the data file. targetcategory: 1 = PA 1000 trial; 2 = SS 1000 trial; 3 = PE 1000 trial; 0 = other testtrial: 0 = practice/training; 1 = testtrial (trialcount >= 20 in each testblock) stimulusitem: the presented stimuli in order of trial presentation response: the participant's response (57 = spacebar; 0 = noresponse) correct: the correctness of the response (1 = correct; 0 = incorrect) latency: the response latency (in ms); measured from onset of stims (2) Summary data file: 'attraction_pgnat_summary*.iqdat' (a separate file for each participant) inquisit.version: Inquisit version run computer.platform: the platform the script was run on (win/mac/ios/android) startDate: date script was run startTime: time script was started subjectid: assigned subject id number groupid: assigned group id number sessionid: assigned session id number elapsedTime: time it took to run script (in ms); measured from onset to offset of script completed: 0 = script was not completed (prematurely aborted); 1 = script was completed (all conditions run) ///////////////////////////////////////////////////////////////////// TARGET 'PA' Conditions signals = Physical Appearance (PA) ///////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////// PA-like Condition: signals = Physical Appearance (PA) OR like noise = anything else (objects OR dislike) //////////////////////////////////////////////////// rHit_PA_like: hit rate for pairing PA-like across all responsetimeouts; test trials only hit: pressing spacebar for signals (Physical Appearance (PA) OR like) in PA-like condition rMiss_PA_like: miss rate for pairing PA-like across all responsetimeouts; test trials only miss: not pressing spacebar for signals (Physical Appearance (PA) OR like) in PA-like condition rFa_PA_like: false alarm (FA) rate for pairing PA-like across all responsetimeouts; test trials only false alarm: pressing spacebar for noise stims (objects OR dislike) in PA-like conditon rCr_PA_like: correct rejection (CR) for pairing PA-like across all responsetimeouts; test trials only cr: not pressing spacebar for noise stims (objects OR dislike) in PA-like condition zHit_PA_like: z-score of hit rate for pairings PA-like (here: Physical Appearance (PA)-like) zFa_PA_like: z-score of FA rate for pairings PA-like (here: Physical Appearance (PA)-like) Note: *Adjustments to z-scores as recommended by: Gregg, A. & Sedikides, C. (2010). Narcissistic Fragility: Rethinking Its Links to Explicit and Implicit Self-esteem, Self and Identity, 9:2, 142-161 (p.148) => Adjustments are made if the FArate (hitRate) = 0 (increased to 0.005) or 1 (decreased to 0.995)* dPrime_PA_like: Computes d' (parametric measure of discriminability btw. signals and noise) for 'Physical Appearance (PA)-like' Pairings => Range (in this script): -5.1516586840152740479 <= dprime <= 5.1516586840152740479 (=perfect performance) => The higher the value, the better signals (go stims) were distinguished from noise (nogo stims) (d' = 0: chance performance; negative d-primes: participant treated nontargets as targets and targets as nontargets) c_PA_like: c-criterion in signal detection:The absolute value of c provides an indication of the strength of the response bias/response style negative: participant more likely to report that signal (go stims) is present (liberal response style); may favor faster responding in speed-accuracy trade-off response paradigms positive: favoring caution (conservative response style) //////////////////////////////////////////////////// PA-dislike Condition: signals = Physical Appearance (PA) OR dislike noise = anything else (objects OR like) //////////////////////////////////////////////////// rHit_PA_dislike: hit rate for pairing PA-dislike across all responsetimeouts; test trials only hit: pressing spacebar for signals (Physical Appearance (PA) OR dislike) in PA-dislike condition rMiss_PA_dislike: miss rate for pairing PA-dislike across all responsetimeouts; test trials only miss: not pressing spacebar for signals (Physical Appearance (PA) OR dislike) in PA-dislike condition rFa_PA_dislike: false alarm (FA) rate for pairing PA-dislike across all responsetimeouts; test trials only false alarm: pressing spacebar for noise stims (objects OR like) in PA-dislike condition rCr_PA_dislike: correct rejection (CR) for pairing PA-dislike across all responsetimeouts; test trials only cr: not pressing spacebar for noise stims (objects OR like) in PA-dislike condition zHit_PA_dislike: z-score of hit rate for pairings PA-dislike (here: Physical Appearance (PA)-dislike) zFa_PA_dislike: z-score of FA rate for pairings PA-dislike (here: Physical Appearance (PA)-dislike) dPrime_PA_dislike: Computes d' (parametric measure of discriminability btw. signals and noise) for 'Physical Appearance (PA)-dislike' Pairings c_PA_dislike: c-criterion in PA-dislike dPrimeDiffTargetPA: the difference in dprime btw. _PA_like and _PA_dislike (_PA_like-_PA_dislike) => if d prime for Physical Appearance (PA)-like is larger than for Physical Appearance (PA)-dislike (positive difference): participant more closely associated PA with like than with dislike attributes => if d prime for Physical Appearance (PA)-like is smaller than for Physical Appearance (PA)-dislike (negative difference): participant more closely associated PA with dislike than with like attributes ///////////////////////////////////////////////////////////////////// TARGET 'SS' Conditions signals = Social Status (SS) ///////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////// SS-like Condition: signals = Social Status (SS) OR like noise = anything else (objects OR dislike) //////////////////////////////////////////////////// rHit_SS_like: hit rate for pairing SS-like across all responsetimeouts; test trials only hit: pressing spacebar for signals (Social Status (SS) OR like) in SS-like condition rMiss_SS_like: miss rate for pairing SS-like across all responsetimeouts; test trials only miss: not pressing spacebar for signals (Social Status (SS) OR like) in SS-like condition rFa_SS_like: false alarm (FA) rate for pairing SS-like across all responsetimeouts; test trials only false alarm: pressing spacebar for noise stims (objects OR dislike) in SS-like condition rCr_SS_like: correct rejection (CR) for pairing SS-like across all responsetimeouts; test trials only cr: not pressing spacebar for noise stims (objects OR dislike) in SS-like condition zHit_SS_like: z-score of hit rate for pairings SS-like (here: Social Status (SS)-like) zFa_SS_like: z-score of FA rate for pairings SS-like (here: Social Status (SS)-like) dPrime_SS_like: Computes d' (parametric measure of discriminability btw. signals and noise) for 'Social Status (SS)-like' Pairings c_SS_like: c-criterion in signal detection in SS-like condition //////////////////////////////////////////////////// SS-dislike Condition: signals = Social Status (SS) OR dislike noise = anything else (objects OR like) //////////////////////////////////////////////////// rHit_SS_dislike: hit rate for pairing SS-dislike across all responsetimeouts; test trials only hit: pressing spacebar for signals (Social Status (SS) OR dislike) in SS-dislike condition rMiss_SS_dislike: miss rate for pairing SS-dislike across all responsetimeouts; test trials only miss: not pressing spacebar for signals (Social Status (SS) OR dislike) in SS-dislike condition rFa_SS_dislike: false alarm (FA) rate for pairing SS-dislike across all responsetimeouts; test trials only false alarm: pressing spacebar for noise stims (objects OR like) in SS-dislike condition rCr_SS_dislike: correct rejection (CR) for pairing SS-dislike across all responsetimeouts; test trials only cr: not pressing spacebar for noise stims (objects OR like) in SS-dislike condition zHit_SS_dislike: z-score of hit rate for pairings SS-dislike (here: Social Status (SS)-dislike) zFa_SS_dislike: z-score of FA rate for pairings SS-dislike (here: Social Status (SS)-dislike) dPrime_SS_dislike: Computes d' (parametric measure of discriminability btw. signals and noise) for 'Social Status (SS)-dislike' Pairings c_SS_dislike: c-criterion in SS-dislike condition dPrimeDiffTargetSS: the difference in dprime btw. _SS_like and _SS_dislike (_SS_like-_SS_dislike) => if d prime for Social Status (SS)-like is larger than for Social Status (SS)-dislike (positive difference): participant more closely associated 'SS' with like than with dislike attributes => if d prime for Social Status (SS)-like is smaller than for Social Status (SS)-dislike (negative difference): participant more closely associated 'SS' with dislike than with like attributes ///////////////////////////////////////////////////////////////////// TARGET 'PE' Conditions signals = Personality (PE) ///////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////// PE-like Condition: signals = Personality (PE) OR like noise = anything else (objects OR dislike) //////////////////////////////////////////////////// rHit_PE_like: hit rate for pairing PE-like across all responsetimeouts; test trials only hit: pressing spacebar for signals (Personality (PE) OR like) in PE-like condition rMiss_PE_like: miss rate for pairing PE-like across all responsetimeouts; test trials only miss: not pressing spacebar for signals (Personality (PE) OR like) in PE-like condition rFa_PE_like: false alarm (FA) rate for pairing PE-like across all responsetimeouts; test trials only false alarm: pressing spacebar for noise stims (objects OR dislike) in PE-like condition rCr_PE_like: correct rejection (CR) for pairing PE-like across all responsetimeouts; test trials only cr: not pressing spacebar for noise stims (objects OR dislike) in PE-like condition zHit_PE_like: z-score of hit rate for pairings PE-like (here: Personality (PE)-like) zFa_PE_like: z-score of FA rate for pairings PE-like (here: Personality (PE)-like) dPrime_PE_like: Computes d' (parametric measure of discriminability btw. signals and noise) for 'Personality (PE)-like' Pairings c_PE_like: c-criterion in signal detection in PE-like condition //////////////////////////////////////////////////// PE-dislike Condition: signals = Personality (PE) OR dislike noise = anything else (objects OR like) //////////////////////////////////////////////////// rHit_PE_dislike: hit rate for pairing PE-dislike across all responsetimeouts; test trials only hit: pressing spacebar for signals (Personality (PE) OR dislike) in PE-dislike condition rMiss_PE_dislike: miss rate for pairing PE-dislike across all responsetimeouts; test trials only miss: not pressing spacebar for signals (Personality (PE) OR dislike) in PE-dislike condition rFa_PE_dislike: false alarm (FA) rate for pairing PE-dislike across all responsetimeouts; test trials only false alarm: pressing spacebar for noise stims (objects OR like) in PE-dislike condition rCr_PE_dislike: correct rejection (CR) for pairing PE-dislike across all responsetimeouts; test trials only cr: not pressing spacebar for noise stims (objects OR like) in PE-dislike condition zHit_PE_dislike: z-score of hit rate for pairings PE-dislike (here: Personality (PE)-dislike) zFa_PE_dislike: z-score of FA rate for pairings PE-dislike (here: Personality (PE)-dislike) dPrime_PE_dislike: Computes d' (parametric measure of discriminability btw. signals and noise) for 'Personality (PE)-dislike' Pairings c_PE_dislike: c-criterion in PE-dislike condition dPrimeDiffTargetPE: the difference in dprime btw. _PE_like and _PE_dislike (_PE_like-_PE_dislike) => if d prime for Personality (PE)-like is larger than for Personality (PE)-dislike (positive difference): participant more closely associated 'PE' with like than with dislike attributes => if d prime for Personality (PE)-like is smaller than for Personality (PE)-dislike (negative difference): ___________________________________________________________________________________________________________________ EXPERIMENTAL SET-UP ___________________________________________________________________________________________________________________ 3 target categories (Physical Appearance (PA), Social Status (SS), and Personality (PE)) x 2 preference categories (like vs. dislike) * 3 practice blocks of categorizing items into 3 target categories Physical Appearance (PA), Social Status (SS), and Personality (PE) => target categories are tested in blocked format, order of blocks is random => 25 trials each * 2 practice blocks of categorizing items into 2 preference categories (Like/Dislike) => preference categories are tested in blocked format, order is random => 20 trials each * 6 test blocks: combination of 3 target categories (PA, SS, PE) x 2 like categories (like, dislike) categorization of items into paired/combined categories (e.g. sort items into "Physical Appearance" OR "I like") => combined categories are tested in blocked format, order of the test blocks is random => 15 practice trials followed by 60 test trials * responsewindow: practice: 750ms test: 1000ms ___________________________________________________________________________________________________________________ STIMULI ___________________________________________________________________________________________________________________ under section Editable Stimuli ___________________________________________________________________________________________________________________ INSTRUCTIONS ___________________________________________________________________________________________________________________ under section Editable Instructions as well as section Instructions ___________________________________________________________________________________________________________________ EDITABLE CODE ___________________________________________________________________________________________________________________ check below for (relatively) easily editable parameters, stimuli, instructions etc. Keep in mind that you can use this script as a template and therefore always "mess" with the entire code to further customize your experiment.